Intersection between ageing-related Genes and SARS-CoV-2 Interactome: Is it higher than expected?

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. Moreover, it has been demonstrated that ageing causes some modi ﬁ cations at a molecular level. Objective: The study aims to investigate a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people. Methods: We considered publicly available datasets on ageing-related genes and SARS-CoV-2 interactors. Then, for each SARS-CoV-2 protein interactor, we tested for the enrichment of ageing-related proteins. Finally, we performed a network-based analysis to identify which molecular mechanisms could play a role in the SARS-CoV-2 molecular aetiology and ultimately affect COVID-19 outcome. Results: We observed a signi ﬁ cant intersection between some SARS-CoV-2 interactors and ageing-related genes. Our analysis evidenced that virus infection particularly affects ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. Conclusion: Our study generated a mechanistic framework aiming at explaining the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. This will provide testable hypotheses for future investigation on the mechanism of action of coronaviruses and pharmacological solutions tailored on speci ﬁ c age ranges. the interactors Gene Ontology is the following terms: (GO:0090402) oncogene-induced cell senescence, (GO:0035986) senescence-associated heterochromatin focus assembly, (GO:2000774 ) positive regulation (GO:2000772)


Introduction
At the end of 2019 in Wuhan (China), medical facilities reported acute pneumonia cases with an unknown origin. Further analysis revealed that a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was responsible for that disease, called coronavirus disease 2019 (COVID-19) [1]. From the beginning, the clinical manifestations spanned from asymptomatic infection to severe pneumonia as well as a severe inflammation state (molecularly characterised by a cytokine cascade [2]) with a fatal outcome [3,4,5,6].
Starting from China, the virus spread in almost all other countries globally, causing infections and deaths. On 11th March 2020, the World Health Organisation (WHO) declared SARS-CoV-2 as a pandemic. Current data [7] revealed that the impact of COVID-19 presents certain peculiar aspects in different nations that have been deeply investigated. Some authors hypothesised that virus mutations were responsible for these differences [8,9,10]. Nevertheless, many independent studies agreed that the mutations did not have a primary role in the differentiation of the outcomes [11,12,13].
Despite the lack of the individuation of the causes, there was a substantial agreement on the fact that the variation of the case fatality rate (CFR), i.e. the fraction of confirmed case with lethal outcome, ranging from 0 to 20% and beyond at country level, should be deeply investigated [14,15,16]. Among the other differences, we focus on the observation that the infection is significantly more lethal in elderly people [17,18,19,20]. Some studies have focused on the possible link between increased mortality rate and some characteristics of elderly people [21,22]. These studies suggested the potential effect of the virus as a trigger activating the decompensation of other chronic conditions [23,24,25,26]. In [27] authors discuss the increased state of inflammation in old people, (called inflammageing), and its possible relation with COVID-19 manifestation that causes the rise of inflammation.
Previous studies [28] have also evidenced that the understanding of modification of molecular mechanism related to the ageing process (i.e. modification of gene expression, modulation of regulatory mechanisms) may reveal important insight about ageing. Many studies contributed to identifying such ageing-related diseases [29,30,31] despite the difficulty of having experimental data [32]. However, wet lab experimental analyses of human ageing are difficult to find. Computational predictions have also been made in [28,33] giving both candidate genes and networks [34,35].
Consequently, the study of the intersection between SARS-CoV-2 and ageing-related molecular alterations could augment the understanding of COVID-19 and then improve its treatment [36]. In [37] a first analysis based on some preliminary public data has reinforced the rationale that there is a possible link.
We start from the consideration that a possible link exists; we hypothesise that SARS-CoV-2 interacting proteins (and genes) may have an overlap with ageing-related genes higher than chance. Therefore, the infection may dysregulate these mechanisms that are yet altered in elderly people causing severe outcomes. We explored such scenario using available public data of expression changes in elderly people GTeX data [38], genes annotated as ageing genes in MSigDB [39], and interacting partners of SARS-CoV-2 described in [40,41].
We observed a significant overlap between the interactors of SARS-CoV-2 and ageing group. Further, we looked at a network-level scenario [35], by considering possible regulatory mechanisms that may be altered [42,43]. These observations support previous reports that SARS-CoV-2 may manifest in the blood leading to multiorgan failure in severe cases of COVID-19.

SARS-CoV-2 Interaction Map
We considered SARS-CoV-2 protein interaction map provided by Gordon et al., [41] and by Guzzi et al., [40]. Both works provided data about 26 of the 29 SARS-CoV-2 proteins behaviour in human cells by identifying the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry. They found high-confidence protein-protein interactions between SARS-CoV-2 and human proteins; they also provided data about possible interactions with an associated reliability score. We considered both high and low confidence interactions.
Ageing Related Genes We first defined and labelled genes (and or proteins) as ageing. We considered data provided from GTEx dataset that contains genes positively and negatively correlated with human age [38]. We gathered data from the GenAge dataset that derived human genes by projecting sequence orthologs in model organisms. We also considered MSigDB, which collected 70 studies associated with ageing. We excluded from the analysis datasets containing data from the brain or other tissues that are not related to COVID-19 manifestation, thus focusing only on lung and kidney data. We only selected datasets reporting experiments from homo sapiens since the projection of orthologs may produce not reliable results for ageing as described in [28].
Bioinformatic Analysis We considered SARS-CoV-2 interacting partners. We measured the intersection between its list of interactors and the ageing-related genes for each viral protein. We estimated the probability that this intersection is higher than chance.
Database of Protein Interactions We used the Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING) [44] that is a freely available repository storing both physical and functional association among proteins. Users may search the database through a web interface by specifying a protein identifier or inserting the primary sequence of a protein. We queried the database using the identifiers of the nodes of each subnetwork. We used medium confidence as the minimum confidence score for each interaction and all for the sources of interactions.
Network Analysis For each subnetwork, we report the main topological parameters: number of nodes, number of edges, average node degree, average local clustering coefficient, the expected number of edges, and PPI enrichment p-value. A p-value lower than 0.01 indicates that the input list of proteins has more interactions than what would be expected for a random set of proteins of similar size, drawn from the genome. For each network we performed a Gene Ontology enrichment analysis, and we take into account only top enriched annotation considering the strength measure. This measure is the log-ratio between i) the number of proteins in each network that are annotated with a term and ii) the number of proteins that we expect to be annotated with this term in a random network of the same size. For each network, we also extracted the main pathways associated in the Reactome database [45]. Network analysis and visualisation were performed in Cytoscape 3.7.0 [46].

Results
We report the results of the significance of intersection of virus interactors and ageing related genes. Table   1 reports the p-values of the intersection. We set as threshold a p value 0:01.
SARS-CoV2 M The analysis revealed that only ten viral proteins have interactors with a significant overlap with respect to ageing related proteins as summarised in Table 1. For each interactor we report human proteins. Figure 1 reports all the subnetworks of SARS-CoV-2 interactors related to ageing.

Interaction networks of the Interactors.
M Subnetwork Figure 2 reports the interactors of the M viral proteins. The subnetwork has 35 nodes and 61 edges. The STRING database reports a PPI enrichment p-value equal to 1.43e-13 as reported in Table 2, therefore there is a significant probability that these interactors may be involved into a set of common functions. Main biological processes are senescence-associated heterochromatin focus assembly (GO:0035986), oncogene-induced cell senescence (GO:0090402), and positive regulation of cell ageing (GO:0090403).
NSP2 Subnetwork The interaction network of NSP2 protein has 25 proteins and 45 edges. It has a relatively high node degree as reported in Table 3 Table 4 and depicted in NSP8 Subnetwork The human ppi subnetwork induced by NSP8 interactors has 18 nodes and 30 edges as reported in Table 6  Orf3a Subnetwork The network associated to Orf3a interactors has 33 nodes and 87 edges as depicted in Figure 9 and as summarised in Table 9. Main biological processes associated to this network are: positive regulation of cellular senescence (GO:2000774), negative regulation of B cell apoptotic process (GO:0002903), and senescence-associated heterochromatin focus assembly (GO:0035986). Pathways associated are Formation of Senescence-Associated Hete-rochromatin (HSA-2559584), Activation of E2F1 target genes at G1/S and CdS1 mediated inactivation of Cyclin B (HSA-75035) .
Orf8 Subnetwork The network associated to Orf8 interactors has 29 nodes and 40 edges as depicted in Figure 10 and as summarised in Table 10 Orf9C Subnetwork The network associated to Orf9c interactors has 42 nodes and 80 edges as depicted in Figure 11 and as reported in Table 11. Main biological processes associated to this network are: senescence-associated heterochromatin focus assembly (

Discussion
As investigated in [47] ageing is characterised by the decline of the immune function. Elderly people are not immuno-deficient, but often the response of their immune system is not sufficient to be effective again antigens. This effect is particularly evident when they are subject to novel antigens. It is known that both responses to influenza and vaccination are not efficient in elderly [48,49]. Moreover, elderly accumulate inflammatory mediators in tissues (inflammageing process), that may occur by the accumulation of DNA lesions that in turn, trigger the increased production of inflammatory mediators [50]. In parallel, the link between COVID-19 and the suppression of the immune system has been observed in [51]. Authors found that many proteins related to the immune response were modulated, causing the possible suppression of such a system.
In this work we observed that some of the genes that are involved in this process (both inflammageing and immune response decline) [52] are targeted by the SARS-CoV-2, thus giving a partial answer to the increased mortality in elderly people. Interestingly, we found that the intersection between targets of SARS-CoV-2 and ageing-related proteins is higher than by chance for some virus proteins (NSP2, NSP4, NSP6, NSP11, NSP13, Orf7a, Orf3a, Orf8, Orf9c and M). This may suggest that virus may cause a different response in elderly people; thus, further experiments may be done to investigate this relation in orthogonal studies. We considered first interactors of each viral protein separately, then we considered interactions between the interactors and finally, we considered the common sub-networks of them.
First of all, it should be noted that the proteins have more interactions among themselves with respect to a random null model as reported by STRING database. It indicates that all these proteins may be biologically connected, i.e. they may belong to some correlated function or pathway. The Gene Ontology analysis revealed that the whole network is enriched with the following terms: ( Moreover, the common sub-network reveals some insights in molecular mechanism related to senescence. The bioinformatics analysis reveals that the subnetwork is associated with previous infectious disease (HIV, SARS-CoV, influenza viruses) [53].
The High Mobility Group A (HMGA) chromatin architectural factors [54] have been associated in the past with the control of a number of pathways involved in cell proliferation and survival. The High Mobility Group A (HMGA) family includes three proteins: HMGA1a, HMGA1b, (produced by alternative splicing of the same gene) and HMGA2. These proteins alter chromatin structure and facilitate the assembly of transcriptional factors [55]. Therefore, they modulate the transcription of several genes involved in a wide spectrum of biological processes, such as embryogenesis, cell differentiation, cell cycle, cell migration, apoptosis and cell transformation. In particular, there is evidence of global changes of chromatin domain from in vitro studies for ageing [56]. In particular, it has been observed that during senescence mammalian cells develop senescence-associated heterochromatin foci as well as high-mobility group A (HMGA) proteins. Therefore, the dysregulation may accelerate cell cycle arrest in response to stress [57].
The elongation factor protein EEF2 promotes the GTP-dependent translocation of the nascent protein chain from the A-site to the P-site of the ribosome. It has been associated in the past with the [58] ageing processes related to the modulation of protein synthesis. In particular, the levels of EEF2 protein were related to longevity. Therefore, the dysregulation may cause the accumulation on DNA damage or on the protein levels accumulating errors in the downstream cascade of mechanisms [59]. The role of EEF2 in severe cases of COVID-19 has also been elucidated in [51]. Thus our study provides another evidence. Moreover, this protein is targeted together with the Eukaryotic translation initiation factor 2 subunit 1 (EIF2S1) by Orf3a, Orf8, NSP2, NSP6, NSP11, NSP13, indicating a possible role of the virus to promote viral translation over cellular translation [60].
The Nucleophosmin (NPM1) is involved in many cellular processes already related to DNA and cell cycle control such as ribosome biogenesis, protein chaperoning, centrosome duplication, histone assembly, and cell proliferation [61,62]. Previous studies investigated the age incidence of acute myeloid leukaemia with mutated nucleophosmin (NPM1) [63,64], while there are no studies related to these mutations and other diseases. Therefore, this work may suggest further studies in such a direction. The interaction between NPM1 and the nucleocapsid protein of the previous SARS-CoV is known to affect the viral particle assembly [65,66,67]. The role of NPM1 and of Histone H2AX targeted by other viral proteins has also been reported in other viruses such as Epstein-Barr and KSHV as a common strategy to manipulate translation and to promote virus latency [68,69].
The DNA (apurinic or apyrimidinic site) lyase (APEX1) plays an important role in the cellular response to oxidative stress. APEX1 has a major role in DNA-repair and in redox regulation of transcription factors [64]. It positively acts in DNA base excision repair (BER) pathway of DNA lesions induced by oxidative and alkylating agents. Together with NPM1, associates with rRNA and Binds DNA and RNA. The modifications in the APEX expression have been correlated with increased senescence [64]. In [70] the synergistic downregulation of both APEX and NPM1 has been clearly observed in oligodendrocyte cells in relation to ageing.
The DNA-dependent protein kinase catalytic subunit (PRKCD) is a Serine/threonine-protein kinase that acts as a molecularsensorfor DNAdamage. Italsoplays aroleintheregulationofDNAvirusmediatedinnateimmuneresponse by assembling into the HDP-RNP complex [71]. This finding is compatible with the consideration that COVID-19 pathogenesis is also related with inflammation-mediated coagulation as a result of inflammatory responses as reported [72].
Also, the CHEK1 is a serine/threonine-protein kinase which is required for checkpoint-mediated cell cycle arrest and activation of DNA repair in response to the presence of DNA damage or unreplicated DNA. It participates in the regulation of a set of mechanisms related to the preservation of the integrity of the genome [73]. The involvement of this protein suggests the severe inhibition of the DNA damage response system during virus infection leading to acute inflammation. Moreover, CHEK1 is targeted together with CDK1 by many SARS-CoV-2 interactors (NSP2, NSP4, NSP11, NSP13) and with CDKN2A (Orf3, NSP13) suggesting an additive effect on the disruption of pathways of apoptosis mediated by TP53 [74] yet disregulated by both senescence and ageing. [75].

Conclusion
We applied a bioinformatic analysis to perform a qualitative analysis of mechanisms of infection by SARS-CoV-2 in elderly people.
Several studies have shown in the past the modifications of genes and proteins that occur in elderly people. Other studies have partially elucidated the mechanism of infections and the dysregulated pathways in CoVID patients.
We detected a statistically significant overlap between SARS-CoV-2 interacting proteins and those related to ageing, suggesting a potentially different response in elderly people. These results will provide an important molecular basis for understanding the mechanism of infections and will shed light on infection progression.            Figure 3